Overview

Dataset statistics

Number of variables21
Number of observations7527
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows583
Duplicate rows (%)7.7%
Total size in memory1.2 MiB
Average record size in memory168.0 B

Variable types

Numeric7
Text6
Categorical8

Alerts

Switch to order menu has constant value ""Constant
Dataset has 583 (7.7%) duplicate rowsDuplicates
Has Table booking is highly imbalanced (66.4%)Imbalance
Rating text is highly imbalanced (59.9%)Imbalance
Average Cost for two is highly skewed (γ1 = 84.5789593)Skewed
Aggregate rating has 129 (1.7%) zerosZeros

Reproduction

Analysis started2024-01-03 22:41:14.133093
Analysis finished2024-01-03 22:41:29.765028
Duration15.63 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Restaurant ID
Real number (ℝ)

Distinct6942
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10556892
Minimum549
Maximum19040277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:30.661307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum549
5-th percentile110395.6
Q13500060
median7701457
Q317147150
95-th percentile18718492
Maximum19040277
Range19039728
Interquartile range (IQR)13647090

Descriptive statistics

Standard deviation7075141.1
Coefficient of variation (CV)0.67019168
Kurtosis-1.7265709
Mean10556892
Median Absolute Deviation (MAD)7634231
Skewness-0.087788496
Sum7.9461726 × 1010
Variance5.0057622 × 1013
MonotonicityNot monotonic
2024-01-03T19:41:31.133149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18691230 3
 
< 0.1%
3900267 3
 
< 0.1%
6310675 2
 
< 0.1%
16949791 2
 
< 0.1%
6102643 2
 
< 0.1%
18586207 2
 
< 0.1%
15078 2
 
< 0.1%
16947027 2
 
< 0.1%
18148977 2
 
< 0.1%
15768 2
 
< 0.1%
Other values (6932) 7505
99.7%
ValueCountFrequency (%)
549 1
< 0.1%
900 1
< 0.1%
1345 1
< 0.1%
2008 1
< 0.1%
4751 2
< 0.1%
5004 1
< 0.1%
5059 1
< 0.1%
6360 1
< 0.1%
7224 1
< 0.1%
7528 1
< 0.1%
ValueCountFrequency (%)
19040277 1
< 0.1%
19035941 1
< 0.1%
19034826 1
< 0.1%
19008239 1
< 0.1%
19003565 1
< 0.1%
19003551 1
< 0.1%
19000219 1
< 0.1%
19000106 1
< 0.1%
18997511 1
< 0.1%
18997447 1
< 0.1%
Distinct5914
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:31.711875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length75
Median length47
Mean length15.156503
Min length3

Characters and Unicode

Total characters114083
Distinct characters181
Distinct categories15 ?
Distinct scripts6 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5123 ?
Unique (%)68.1%

Sample

1st rowMama Lou's Italian Kitchen
2nd rowMama Lou's Italian Kitchen
3rd rowBlackbird
4th rowBanapple
5th rowBad Bird
ValueCountFrequency (%)
746
 
4.0%
the 639
 
3.4%
restaurant 582
 
3.1%
cafe 514
 
2.7%
pizza 351
 
1.9%
bar 244
 
1.3%
domino's 208
 
1.1%
kitchen 186
 
1.0%
house 160
 
0.9%
grill 155
 
0.8%
Other values (6269) 14959
79.8%
2024-01-03T19:41:32.770634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12046
 
10.6%
11292
 
9.9%
e 9160
 
8.0%
i 6352
 
5.6%
o 6110
 
5.4%
r 6075
 
5.3%
n 5613
 
4.9%
s 5170
 
4.5%
t 4952
 
4.3%
l 3665
 
3.2%
Other values (171) 43648
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81180
71.2%
Uppercase Letter 18414
 
16.1%
Space Separator 11292
 
9.9%
Other Punctuation 2085
 
1.8%
Dash Punctuation 423
 
0.4%
Decimal Number 344
 
0.3%
Other Letter 282
 
0.2%
Math Symbol 16
 
< 0.1%
Final Punctuation 13
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Other values (5) 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 36
12.8%
ي 30
 
10.6%
ب 19
 
6.7%
و 19
 
6.7%
ل 18
 
6.4%
ر 18
 
6.4%
ز 17
 
6.0%
ك 16
 
5.7%
ت 15
 
5.3%
ش 9
 
3.2%
Other values (51) 85
30.1%
Lowercase Letter
ValueCountFrequency (%)
a 12046
14.8%
e 9160
11.3%
i 6352
 
7.8%
o 6110
 
7.5%
r 6075
 
7.5%
n 5613
 
6.9%
s 5170
 
6.4%
t 4952
 
6.1%
l 3665
 
4.5%
u 3363
 
4.1%
Other values (37) 18674
23.0%
Uppercase Letter
ValueCountFrequency (%)
C 2043
 
11.1%
B 2029
 
11.0%
S 1441
 
7.8%
T 1357
 
7.4%
P 1209
 
6.6%
R 1184
 
6.4%
M 1037
 
5.6%
D 984
 
5.3%
H 848
 
4.6%
F 782
 
4.2%
Other values (23) 5500
29.9%
Other Punctuation
ValueCountFrequency (%)
' 1476
70.8%
& 446
 
21.4%
. 107
 
5.1%
, 13
 
0.6%
@ 12
 
0.6%
! 12
 
0.6%
: 7
 
0.3%
" 4
 
0.2%
/ 2
 
0.1%
¿ 2
 
0.1%
Other values (3) 4
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 75
21.8%
2 52
15.1%
3 35
10.2%
0 32
9.3%
9 29
 
8.4%
7 28
 
8.1%
6 27
 
7.8%
4 25
 
7.3%
5 23
 
6.7%
8 18
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 417
98.6%
6
 
1.4%
Math Symbol
ValueCountFrequency (%)
+ 15
93.8%
| 1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 11
91.7%
{ 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 11
91.7%
} 1
 
8.3%
Format
ValueCountFrequency (%)
2
66.7%
1
33.3%
Other Symbol
ValueCountFrequency (%)
° 2
66.7%
1
33.3%
Nonspacing Mark
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
11292
100.0%
Final Punctuation
ValueCountFrequency (%)
13
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 99594
87.3%
Common 14205
 
12.5%
Arabic 240
 
0.2%
Han 34
 
< 0.1%
Hangul 5
 
< 0.1%
Devanagari 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12046
 
12.1%
e 9160
 
9.2%
i 6352
 
6.4%
o 6110
 
6.1%
r 6075
 
6.1%
n 5613
 
5.6%
s 5170
 
5.2%
t 4952
 
5.0%
l 3665
 
3.7%
u 3363
 
3.4%
Other values (70) 37088
37.2%
Common
ValueCountFrequency (%)
11292
79.5%
' 1476
 
10.4%
& 446
 
3.1%
- 417
 
2.9%
. 107
 
0.8%
1 75
 
0.5%
2 52
 
0.4%
3 35
 
0.2%
0 32
 
0.2%
9 29
 
0.2%
Other values (28) 244
 
1.7%
Han
ValueCountFrequency (%)
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (19) 19
55.9%
Arabic
ValueCountFrequency (%)
ا 36
15.0%
ي 30
12.5%
ب 19
7.9%
و 19
7.9%
ل 18
7.5%
ر 18
7.5%
ز 17
 
7.1%
ك 16
 
6.7%
ت 15
 
6.2%
ش 9
 
3.8%
Other values (14) 43
17.9%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Devanagari
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113447
99.4%
None 329
 
0.3%
Arabic 240
 
0.2%
CJK 34
 
< 0.1%
Punctuation 22
 
< 0.1%
Hangul 5
 
< 0.1%
Devanagari 5
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12046
 
10.6%
11292
 
10.0%
e 9160
 
8.1%
i 6352
 
5.6%
o 6110
 
5.4%
r 6075
 
5.4%
n 5613
 
4.9%
s 5170
 
4.6%
t 4952
 
4.4%
l 3665
 
3.2%
Other values (73) 43012
37.9%
None
ValueCountFrequency (%)
é 107
32.5%
ı 50
15.2%
ü 26
 
7.9%
ğ 19
 
5.8%
á 16
 
4.9%
ö 11
 
3.3%
ş 11
 
3.3%
ç 10
 
3.0%
ã 10
 
3.0%
ô 9
 
2.7%
Other values (20) 60
18.2%
Arabic
ValueCountFrequency (%)
ا 36
15.0%
ي 30
12.5%
ب 19
7.9%
و 19
7.9%
ل 18
7.5%
ر 18
7.5%
ز 17
 
7.1%
ك 16
 
6.7%
ت 15
 
6.2%
ش 9
 
3.8%
Other values (14) 43
17.9%
Punctuation
ValueCountFrequency (%)
13
59.1%
6
27.3%
2
 
9.1%
1
 
4.5%
CJK
ValueCountFrequency (%)
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (19) 19
55.9%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Devanagari
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Country Code
Real number (ℝ)

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.065365
Minimum1
Maximum216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:33.023314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median30
Q3214
95-th percentile216
Maximum216
Range215
Interquartile range (IQR)213

Descriptive statistics

Standard deviation99.031229
Coefficient of variation (CV)1.064104
Kurtosis-1.8491269
Mean93.065365
Median Absolute Deviation (MAD)29
Skewness0.25868746
Sum700503
Variance9807.1844
MonotonicityNot monotonic
2024-01-03T19:41:33.243840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 3507
46.6%
216 1395
 
18.5%
215 437
 
5.8%
189 382
 
5.1%
214 334
 
4.4%
30 261
 
3.5%
148 253
 
3.4%
14 180
 
2.4%
37 180
 
2.4%
208 165
 
2.2%
Other values (5) 433
 
5.8%
ValueCountFrequency (%)
1 3507
46.6%
14 180
 
2.4%
30 261
 
3.5%
37 180
 
2.4%
94 82
 
1.1%
148 253
 
3.4%
162 88
 
1.2%
166 94
 
1.2%
184 82
 
1.1%
189 382
 
5.1%
ValueCountFrequency (%)
216 1395
18.5%
215 437
 
5.8%
214 334
 
4.4%
208 165
 
2.2%
191 87
 
1.2%
189 382
 
5.1%
184 82
 
1.1%
166 94
 
1.2%
162 88
 
1.2%
148 253
 
3.4%

City
Text

Distinct125
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:33.670681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.715823
Min length3

Characters and Unicode

Total characters58077
Distinct characters53
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowLas Piñas City
2nd rowLas Piñas City
3rd rowMakati City
4th rowMakati City
5th rowMakati City
ValueCountFrequency (%)
city 230
 
2.6%
new 188
 
2.1%
san 181
 
2.1%
nagpur 99
 
1.1%
dhabi 95
 
1.1%
abu 95
 
1.1%
birmingham 95
 
1.1%
surat 95
 
1.1%
goa 94
 
1.1%
cape 94
 
1.1%
Other values (126) 7500
85.6%
2024-01-03T19:41:34.630264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8447
 
14.5%
n 4250
 
7.3%
o 3867
 
6.7%
r 3706
 
6.4%
i 3592
 
6.2%
e 3201
 
5.5%
t 2664
 
4.6%
h 2626
 
4.5%
l 2351
 
4.0%
u 2219
 
3.8%
Other values (43) 21154
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48104
82.8%
Uppercase Letter 8734
 
15.0%
Space Separator 1239
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8447
17.6%
n 4250
 
8.8%
o 3867
 
8.0%
r 3706
 
7.7%
i 3592
 
7.5%
e 3201
 
6.7%
t 2664
 
5.5%
h 2626
 
5.5%
l 2351
 
4.9%
u 2219
 
4.6%
Other values (17) 11181
23.2%
Uppercase Letter
ValueCountFrequency (%)
A 1025
11.7%
C 919
10.5%
D 851
 
9.7%
S 725
 
8.3%
P 704
 
8.1%
B 570
 
6.5%
M 521
 
6.0%
L 384
 
4.4%
N 383
 
4.4%
G 379
 
4.3%
Other values (15) 2273
26.0%
Space Separator
ValueCountFrequency (%)
1239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56838
97.9%
Common 1239
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8447
14.9%
n 4250
 
7.5%
o 3867
 
6.8%
r 3706
 
6.5%
i 3592
 
6.3%
e 3201
 
5.6%
t 2664
 
4.7%
h 2626
 
4.6%
l 2351
 
4.1%
u 2219
 
3.9%
Other values (42) 19915
35.0%
Common
ValueCountFrequency (%)
1239
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57821
99.6%
None 256
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8447
14.6%
n 4250
 
7.4%
o 3867
 
6.7%
r 3706
 
6.4%
i 3592
 
6.2%
e 3201
 
5.5%
t 2664
 
4.6%
h 2626
 
4.5%
l 2351
 
4.1%
u 2219
 
3.8%
Other values (39) 20898
36.1%
None
ValueCountFrequency (%)
í 86
33.6%
İ 85
33.2%
ã 83
32.4%
ñ 2
 
0.8%
Distinct6760
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:35.226125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length156
Median length108
Mean length53.950711
Min length9

Characters and Unicode

Total characters406087
Distinct characters110
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6087 ?
Unique (%)80.9%

Sample

1st rowBlock 1, Lot 36, Tropical Avenue Corner Tropical Palace, BF International, Las Piñas City
2nd rowBlock 1, Lot 36, Tropical Avenue Corner Tropical Palace, BF International, Las Piñas City
3rd rowNielson Tower, Ayala Triangle Gardens, Salcedo Village, Makati City
4th rowAyala Triangle Gardens, Salcedo Village, Makati City
5th rowHole In The Wall, Floor 4, Century City Mall, Kalayaan Avenue, Poblacion, Makati City
ValueCountFrequency (%)
road 2285
 
3.7%
street 1555
 
2.5%
floor 849
 
1.4%
near 805
 
1.3%
nagar 704
 
1.1%
opposite 558
 
0.9%
mall 513
 
0.8%
avenue 498
 
0.8%
city 469
 
0.8%
ground 439
 
0.7%
Other values (11115) 53404
86.0%
2024-01-03T19:41:36.356952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54617
 
13.4%
a 39067
 
9.6%
e 23517
 
5.8%
, 21548
 
5.3%
r 20903
 
5.1%
o 20138
 
5.0%
n 16663
 
4.1%
i 16226
 
4.0%
t 15880
 
3.9%
l 14818
 
3.6%
Other values (100) 162710
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 242308
59.7%
Uppercase Letter 56823
 
14.0%
Space Separator 54620
 
13.5%
Decimal Number 28121
 
6.9%
Other Punctuation 23219
 
5.7%
Dash Punctuation 874
 
0.2%
Close Punctuation 47
 
< 0.1%
Open Punctuation 47
 
< 0.1%
Control 22
 
< 0.1%
Format 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39067
16.1%
e 23517
9.7%
r 20903
 
8.6%
o 20138
 
8.3%
n 16663
 
6.9%
i 16226
 
6.7%
t 15880
 
6.6%
l 14818
 
6.1%
d 10418
 
4.3%
h 9646
 
4.0%
Other values (32) 55032
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 6510
 
11.5%
C 5154
 
9.1%
A 4176
 
7.3%
B 4059
 
7.1%
M 4001
 
7.0%
R 3924
 
6.9%
N 3540
 
6.2%
P 3134
 
5.5%
G 2594
 
4.6%
H 2082
 
3.7%
Other values (24) 17649
31.1%
Other Punctuation
ValueCountFrequency (%)
, 21548
92.8%
/ 681
 
2.9%
. 568
 
2.4%
& 256
 
1.1%
' 65
 
0.3%
# 51
 
0.2%
: 28
 
0.1%
; 13
 
0.1%
! 4
 
< 0.1%
" 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 5850
20.8%
0 3921
13.9%
2 3810
13.5%
3 2904
10.3%
4 2212
 
7.9%
5 2125
 
7.6%
7 2019
 
7.2%
8 1817
 
6.5%
9 1801
 
6.4%
6 1662
 
5.9%
Space Separator
ValueCountFrequency (%)
54617
> 99.9%
  3
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 873
99.9%
1
 
0.1%
Format
ValueCountFrequency (%)
2
66.7%
­ 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%
Control
ValueCountFrequency (%)
22
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Other Letter
ValueCountFrequency (%)
º 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 299132
73.7%
Common 106955
 
26.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 39067
 
13.1%
e 23517
 
7.9%
r 20903
 
7.0%
o 20138
 
6.7%
n 16663
 
5.6%
i 16226
 
5.4%
t 15880
 
5.3%
l 14818
 
5.0%
d 10418
 
3.5%
h 9646
 
3.2%
Other values (67) 111856
37.4%
Common
ValueCountFrequency (%)
54617
51.1%
, 21548
 
20.1%
1 5850
 
5.5%
0 3921
 
3.7%
2 3810
 
3.6%
3 2904
 
2.7%
4 2212
 
2.1%
5 2125
 
2.0%
7 2019
 
1.9%
8 1817
 
1.7%
Other values (23) 6132
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 405052
99.7%
None 1031
 
0.3%
Punctuation 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54617
 
13.5%
a 39067
 
9.6%
e 23517
 
5.8%
, 21548
 
5.3%
r 20903
 
5.2%
o 20138
 
5.0%
n 16663
 
4.1%
i 16226
 
4.0%
t 15880
 
3.9%
l 14818
 
3.7%
Other values (69) 161675
39.9%
None
ValueCountFrequency (%)
ı 143
13.9%
ş 141
13.7%
ã 117
11.3%
İ 98
9.5%
í 98
9.5%
Ç 80
7.8%
ğ 55
 
5.3%
é 51
 
4.9%
ç 46
 
4.5%
ü 45
 
4.4%
Other values (18) 157
15.2%
Punctuation
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Distinct2272
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:36.885291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length70
Median length54
Mean length13.484124
Min length2

Characters and Unicode

Total characters101495
Distinct characters94
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1103 ?
Unique (%)14.7%

Sample

1st rowBF International
2nd rowBF International
3rd rowAyala Triangle Gardens, Salcedo Village, Makati City
4th rowAyala Triangle Gardens, Salcedo Village, Makati City
5th rowCentury City Mall, Poblacion, Makati City
ValueCountFrequency (%)
nagar 454
 
2.9%
city 329
 
2.1%
mall 263
 
1.7%
al 250
 
1.6%
road 249
 
1.6%
street 193
 
1.2%
town 172
 
1.1%
centre 169
 
1.1%
the 154
 
1.0%
hill 151
 
1.0%
Other values (2464) 13135
84.6%
2024-01-03T19:41:37.729013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12360
 
12.2%
8023
 
7.9%
e 6875
 
6.8%
r 6383
 
6.3%
i 5620
 
5.5%
n 5577
 
5.5%
l 5567
 
5.5%
o 5256
 
5.2%
t 5077
 
5.0%
h 2680
 
2.6%
Other values (84) 38077
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75317
74.2%
Uppercase Letter 16056
 
15.8%
Space Separator 8023
 
7.9%
Other Punctuation 1556
 
1.5%
Decimal Number 443
 
0.4%
Dash Punctuation 68
 
0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12360
16.4%
e 6875
 
9.1%
r 6383
 
8.5%
i 5620
 
7.5%
n 5577
 
7.4%
l 5567
 
7.4%
o 5256
 
7.0%
t 5077
 
6.7%
h 2680
 
3.6%
d 2475
 
3.3%
Other values (31) 17447
23.2%
Uppercase Letter
ValueCountFrequency (%)
C 1770
 
11.0%
S 1527
 
9.5%
M 1327
 
8.3%
A 1104
 
6.9%
B 1019
 
6.3%
N 938
 
5.8%
P 909
 
5.7%
T 769
 
4.8%
G 736
 
4.6%
H 712
 
4.4%
Other values (21) 5245
32.7%
Decimal Number
ValueCountFrequency (%)
3 87
19.6%
0 73
16.5%
1 59
13.3%
5 53
12.0%
2 46
10.4%
7 40
9.0%
4 33
 
7.4%
6 32
 
7.2%
9 13
 
2.9%
8 7
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 1395
89.7%
/ 70
 
4.5%
' 37
 
2.4%
& 27
 
1.7%
. 17
 
1.1%
! 4
 
0.3%
@ 3
 
0.2%
: 3
 
0.2%
Space Separator
ValueCountFrequency (%)
8023
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91373
90.0%
Common 10122
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12360
 
13.5%
e 6875
 
7.5%
r 6383
 
7.0%
i 5620
 
6.2%
n 5577
 
6.1%
l 5567
 
6.1%
o 5256
 
5.8%
t 5077
 
5.6%
h 2680
 
2.9%
d 2475
 
2.7%
Other values (62) 33503
36.7%
Common
ValueCountFrequency (%)
8023
79.3%
, 1395
 
13.8%
3 87
 
0.9%
0 73
 
0.7%
/ 70
 
0.7%
- 68
 
0.7%
1 59
 
0.6%
5 53
 
0.5%
2 46
 
0.5%
7 40
 
0.4%
Other values (12) 208
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101210
99.7%
None 285
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12360
 
12.2%
8023
 
7.9%
e 6875
 
6.8%
r 6383
 
6.3%
i 5620
 
5.6%
n 5577
 
5.5%
l 5567
 
5.5%
o 5256
 
5.2%
t 5077
 
5.0%
h 2680
 
2.6%
Other values (63) 37792
37.3%
None
ValueCountFrequency (%)
ı 55
19.3%
ş 39
13.7%
ö 32
11.2%
é 28
9.8%
Ç 21
 
7.4%
ç 21
 
7.4%
ü 20
 
7.0%
ã 15
 
5.3%
Ü 8
 
2.8%
Á 7
 
2.5%
Other values (11) 39
13.7%
Distinct2357
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:38.167369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length82
Median length65
Mean length23.021257
Min length4

Characters and Unicode

Total characters173281
Distinct characters95
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1150 ?
Unique (%)15.3%

Sample

1st rowBF International, Las Piñas City
2nd rowBF International, Las Piñas City
3rd rowAyala Triangle Gardens, Salcedo Village, Makati City, Makati City
4th rowAyala Triangle Gardens, Salcedo Village, Makati City, Makati City
5th rowCentury City Mall, Poblacion, Makati City, Makati City
ValueCountFrequency (%)
city 514
 
2.1%
nagar 454
 
1.9%
town 266
 
1.1%
mall 263
 
1.1%
al 250
 
1.0%
road 249
 
1.0%
street 193
 
0.8%
new 192
 
0.8%
san 190
 
0.8%
centre 169
 
0.7%
Other values (2525) 21362
88.6%
2024-01-03T19:41:39.081699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20776
 
12.0%
16588
 
9.6%
r 9989
 
5.8%
e 9928
 
5.7%
n 9778
 
5.6%
i 9158
 
5.3%
o 9029
 
5.2%
, 8826
 
5.1%
l 7872
 
4.5%
t 7641
 
4.4%
Other values (85) 63696
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 122518
70.7%
Uppercase Letter 24645
 
14.2%
Space Separator 16588
 
9.6%
Other Punctuation 8987
 
5.2%
Decimal Number 443
 
0.3%
Dash Punctuation 68
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20776
17.0%
r 9989
 
8.2%
e 9928
 
8.1%
n 9778
 
8.0%
i 9158
 
7.5%
o 9029
 
7.4%
l 7872
 
6.4%
t 7641
 
6.2%
h 5251
 
4.3%
u 4575
 
3.7%
Other values (32) 28521
23.3%
Uppercase Letter
ValueCountFrequency (%)
C 2644
 
10.7%
S 2303
 
9.3%
A 2161
 
8.8%
M 1848
 
7.5%
B 1589
 
6.4%
P 1558
 
6.3%
D 1486
 
6.0%
N 1276
 
5.2%
G 1115
 
4.5%
L 996
 
4.0%
Other values (21) 7669
31.1%
Decimal Number
ValueCountFrequency (%)
3 87
19.6%
0 73
16.5%
1 59
13.3%
5 53
12.0%
2 46
10.4%
7 40
9.0%
4 33
 
7.4%
6 32
 
7.2%
9 13
 
2.9%
8 7
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 8826
98.2%
/ 70
 
0.8%
' 37
 
0.4%
& 27
 
0.3%
. 17
 
0.2%
! 4
 
< 0.1%
@ 3
 
< 0.1%
: 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
16588
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 147163
84.9%
Common 26118
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20776
 
14.1%
r 9989
 
6.8%
e 9928
 
6.7%
n 9778
 
6.6%
i 9158
 
6.2%
o 9029
 
6.1%
l 7872
 
5.3%
t 7641
 
5.2%
h 5251
 
3.6%
u 4575
 
3.1%
Other values (63) 53166
36.1%
Common
ValueCountFrequency (%)
16588
63.5%
, 8826
33.8%
3 87
 
0.3%
0 73
 
0.3%
/ 70
 
0.3%
- 68
 
0.3%
1 59
 
0.2%
5 53
 
0.2%
2 46
 
0.2%
7 40
 
0.2%
Other values (12) 208
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172740
99.7%
None 541
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20776
 
12.0%
16588
 
9.6%
r 9989
 
5.8%
e 9928
 
5.7%
n 9778
 
5.7%
i 9158
 
5.3%
o 9029
 
5.2%
, 8826
 
5.1%
l 7872
 
4.6%
t 7641
 
4.4%
Other values (63) 63155
36.6%
None
ValueCountFrequency (%)
ã 98
18.1%
í 89
16.5%
İ 87
16.1%
ı 55
10.2%
ş 39
 
7.2%
ö 32
 
5.9%
é 28
 
5.2%
Ç 21
 
3.9%
ç 21
 
3.9%
ü 20
 
3.7%
Other values (12) 51
9.4%

Longitude
Real number (ℝ)

Distinct6846
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.764092
Minimum-122.70046
Maximum175.31055
Zeros1
Zeros (%)< 0.1%
Negative2273
Negative (%)30.2%
Memory size58.9 KiB
2024-01-03T19:41:39.417689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-122.70046
5-th percentile-114.0794
Q1-4.2581417
median73.785121
Q379.833706
95-th percentile121.07513
Maximum175.31055
Range298.01101
Interquartile range (IQR)84.091847

Descriptive statistics

Standard deviation77.395241
Coefficient of variation (CV)2.2922352
Kurtosis-0.59671359
Mean33.764092
Median Absolute Deviation (MAD)18.476102
Skewness-0.68077465
Sum254142.32
Variance5990.0234
MonotonicityNot monotonic
2024-01-03T19:41:39.699495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.894286 6
 
0.1%
-1.914805 5
 
0.1%
31.087159 4
 
0.1%
51.4465452 4
 
0.1%
85.11494368 3
 
< 0.1%
31.09 3
 
< 0.1%
83.003285 3
 
< 0.1%
28.23370333 3
 
< 0.1%
79.843575 3
 
< 0.1%
-1.905477 3
 
< 0.1%
Other values (6836) 7490
99.5%
ValueCountFrequency (%)
-122.7004552 1
< 0.1%
-122.6987207 1
< 0.1%
-122.6984342 1
< 0.1%
-122.6958054 1
< 0.1%
-122.69359 1
< 0.1%
-122.69065 1
< 0.1%
-122.6852592 1
< 0.1%
-122.68458 1
< 0.1%
-122.6842299 1
< 0.1%
-122.68411 1
< 0.1%
ValueCountFrequency (%)
175.3105525 1
< 0.1%
175.3086833 1
< 0.1%
175.305411 1
< 0.1%
175.3 1
< 0.1%
175.299844 1
< 0.1%
175.2943211 1
< 0.1%
175.2942675 1
< 0.1%
175.2939533 1
< 0.1%
175.2937827 1
< 0.1%
175.2937173 1
< 0.1%

Latitude
Real number (ℝ)

Distinct6833
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.332787
Minimum-41.330428
Maximum55.97698
Zeros1
Zeros (%)< 0.1%
Negative1155
Negative (%)15.3%
Memory size58.9 KiB
2024-01-03T19:41:39.983954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-41.330428
5-th percentile-33.918948
Q112.923378
median25.246955
Q331.636552
95-th percentile51.515008
Maximum55.97698
Range97.307408
Interquartile range (IQR)18.713174

Descriptive statistics

Standard deviation23.255979
Coefficient of variation (CV)1.2029295
Kurtosis0.61702577
Mean19.332787
Median Absolute Deviation (MAD)8.7958751
Skewness-1.0829879
Sum145517.89
Variance540.84055
MonotonicityNot monotonic
2024-01-03T19:41:40.285497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.477633 6
 
0.1%
52.477693 5
 
0.1%
53.48083333 5
 
0.1%
-29.727597 4
 
0.1%
25.2601995 4
 
0.1%
-41.29416667 4
 
0.1%
-15.82566667 3
 
< 0.1%
-29.544627 3
 
< 0.1%
19.87689416 3
 
< 0.1%
31.10351202 3
 
< 0.1%
Other values (6823) 7487
99.5%
ValueCountFrequency (%)
-41.330428 1
< 0.1%
-41.32833392 1
< 0.1%
-41.314213 1
< 0.1%
-41.3139 1
< 0.1%
-41.29802315 1
< 0.1%
-41.296494 1
< 0.1%
-41.296155 1
< 0.1%
-41.296107 1
< 0.1%
-41.29597 2
< 0.1%
-41.29556241 1
< 0.1%
ValueCountFrequency (%)
55.97698 1
< 0.1%
55.97649334 1
< 0.1%
55.97545278 1
< 0.1%
55.97509722 2
< 0.1%
55.97483333 1
< 0.1%
55.96466944 1
< 0.1%
55.960552 1
< 0.1%
55.95916111 1
< 0.1%
55.95868333 1
< 0.1%
55.958538 1
< 0.1%
Distinct2832
Distinct (%)37.7%
Missing15
Missing (%)0.2%
Memory size58.9 KiB
2024-01-03T19:41:40.785883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length93
Median length77
Mean length22.053248
Min length3

Characters and Unicode

Total characters165664
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2036 ?
Unique (%)27.1%

Sample

1st rowItalian
2nd rowItalian
3rd rowEuropean, Asian
4th rowFilipino, American, Italian, Bakery
5th rowAmerican
ValueCountFrequency (%)
indian 2495
 
11.0%
food 1800
 
7.9%
north 1703
 
7.5%
chinese 1304
 
5.7%
fast 1102
 
4.9%
italian 1002
 
4.4%
american 851
 
3.8%
cafe 807
 
3.6%
pizza 750
 
3.3%
continental 692
 
3.0%
Other values (190) 10186
44.9%
2024-01-03T19:41:41.716939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 16380
 
9.9%
15180
 
9.2%
n 14705
 
8.9%
e 13491
 
8.1%
i 11873
 
7.2%
, 10554
 
6.4%
o 8949
 
5.4%
t 8447
 
5.1%
r 8250
 
5.0%
s 6430
 
3.9%
Other values (46) 51405
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116803
70.5%
Uppercase Letter 23034
 
13.9%
Space Separator 15180
 
9.2%
Other Punctuation 10561
 
6.4%
Dash Punctuation 86
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 16380
14.0%
n 14705
12.6%
e 13491
11.6%
i 11873
10.2%
o 8949
7.7%
t 8447
7.2%
r 8250
7.1%
s 6430
 
5.5%
d 5697
 
4.9%
h 5228
 
4.5%
Other values (18) 17353
14.9%
Uppercase Letter
ValueCountFrequency (%)
I 3675
16.0%
F 3290
14.3%
C 3278
14.2%
B 2224
9.7%
S 2168
9.4%
N 1787
7.8%
A 1418
 
6.2%
M 1281
 
5.6%
P 897
 
3.9%
D 643
 
2.8%
Other values (14) 2373
10.3%
Other Punctuation
ValueCountFrequency (%)
, 10554
99.9%
' 7
 
0.1%
Space Separator
ValueCountFrequency (%)
15180
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 139837
84.4%
Common 25827
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 16380
11.7%
n 14705
 
10.5%
e 13491
 
9.6%
i 11873
 
8.5%
o 8949
 
6.4%
t 8447
 
6.0%
r 8250
 
5.9%
s 6430
 
4.6%
d 5697
 
4.1%
h 5228
 
3.7%
Other values (42) 40387
28.9%
Common
ValueCountFrequency (%)
15180
58.8%
, 10554
40.9%
- 86
 
0.3%
' 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165651
> 99.9%
None 13
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 16380
 
9.9%
15180
 
9.2%
n 14705
 
8.9%
e 13491
 
8.1%
i 11873
 
7.2%
, 10554
 
6.4%
o 8949
 
5.4%
t 8447
 
5.1%
r 8250
 
5.0%
s 6430
 
3.9%
Other values (43) 51392
31.0%
None
ValueCountFrequency (%)
ö 11
84.6%
ç 1
 
7.7%
é 1
 
7.7%

Average Cost for two
Real number (ℝ)

SKEWED 

Distinct156
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7152.1133
Minimum0
Maximum25000017
Zeros56
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:42.121832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q160
median290
Q3600
95-th percentile1500
Maximum25000017
Range25000017
Interquartile range (IQR)540

Descriptive statistics

Standard deviation290606.83
Coefficient of variation (CV)40.632302
Kurtosis7271.9317
Mean7152.1133
Median Absolute Deviation (MAD)240
Skewness84.578959
Sum53833957
Variance8.4452329 × 1010
MonotonicityNot monotonic
2024-01-03T19:41:42.538317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 518
 
6.9%
400 399
 
5.3%
25 384
 
5.1%
40 373
 
5.0%
300 347
 
4.6%
600 321
 
4.3%
700 299
 
4.0%
50 245
 
3.3%
800 242
 
3.2%
200 221
 
2.9%
Other values (146) 4178
55.5%
ValueCountFrequency (%)
0 56
 
0.7%
10 120
 
1.6%
12 3
 
< 0.1%
15 57
 
0.8%
20 60
 
0.8%
25 384
5.1%
30 156
2.1%
35 144
 
1.9%
38 1
 
< 0.1%
40 373
5.0%
ValueCountFrequency (%)
25000017 1
 
< 0.1%
1200000 2
 
< 0.1%
700000 1
 
< 0.1%
600000 2
 
< 0.1%
550000 3
 
< 0.1%
500000 2
 
< 0.1%
450000 2
 
< 0.1%
400000 6
0.1%
350000 12
0.2%
330000 1
 
< 0.1%

Currency
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
Indian Rupees(Rs.)
3507 
Dollar($)
1837 
Pounds(£)
437 
Rand(R)
382 
Emirati Diram(AED)
 
334
Other values (7)
1030 

Length

Max length22
Median length18
Mean length14.528232
Min length7

Characters and Unicode

Total characters109354
Distinct characters37
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBotswana Pula(P)
2nd rowBotswana Pula(P)
3rd rowBotswana Pula(P)
4th rowBotswana Pula(P)
5th rowBotswana Pula(P)

Common Values

ValueCountFrequency (%)
Indian Rupees(Rs.) 3507
46.6%
Dollar($) 1837
24.4%
Pounds(£) 437
 
5.8%
Rand(R) 382
 
5.1%
Emirati Diram(AED) 334
 
4.4%
Brazilian Real(R$) 261
 
3.5%
NewZealand($) 253
 
3.4%
Turkish Lira(TL) 165
 
2.2%
Qatari Rial(QR) 94
 
1.2%
Botswana Pula(P) 88
 
1.2%
Other values (2) 169
 
2.2%

Length

2024-01-03T19:41:43.027447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
indian 3507
28.7%
rupees(rs 3507
28.7%
dollar 1837
15.0%
pounds(£ 437
 
3.6%
rand(r 382
 
3.1%
emirati 334
 
2.7%
diram(aed 334
 
2.7%
brazilian 261
 
2.1%
real(r 261
 
2.1%
newzealand 253
 
2.1%
Other values (11) 1119
 
9.1%

Most occurring characters

ValueCountFrequency (%)
n 8855
 
8.1%
R 8826
 
8.1%
a 8732
 
8.0%
e 8037
 
7.3%
s 7786
 
7.1%
) 7527
 
6.9%
( 7527
 
6.9%
i 5800
 
5.3%
4705
 
4.3%
d 4661
 
4.3%
Other values (27) 36898
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64550
59.0%
Uppercase Letter 18750
 
17.1%
Close Punctuation 7527
 
6.9%
Open Punctuation 7527
 
6.9%
Space Separator 4705
 
4.3%
Other Punctuation 3507
 
3.2%
Currency Symbol 2788
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 8855
13.7%
a 8732
13.5%
e 8037
12.5%
s 7786
12.1%
i 5800
9.0%
d 4661
7.2%
l 4631
7.2%
u 4366
6.8%
p 3676
5.7%
r 3277
 
5.1%
Other values (7) 4729
7.3%
Uppercase Letter
ValueCountFrequency (%)
R 8826
47.1%
I 3671
19.6%
D 2587
 
13.8%
E 668
 
3.6%
P 613
 
3.3%
L 504
 
2.7%
B 349
 
1.9%
A 334
 
1.8%
T 330
 
1.8%
N 253
 
1.3%
Other values (4) 615
 
3.3%
Currency Symbol
ValueCountFrequency (%)
$ 2351
84.3%
£ 437
 
15.7%
Close Punctuation
ValueCountFrequency (%)
) 7527
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7527
100.0%
Space Separator
ValueCountFrequency (%)
4705
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83300
76.2%
Common 26054
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 8855
10.6%
R 8826
10.6%
a 8732
10.5%
e 8037
9.6%
s 7786
9.3%
i 5800
 
7.0%
d 4661
 
5.6%
l 4631
 
5.6%
u 4366
 
5.2%
p 3676
 
4.4%
Other values (21) 17930
21.5%
Common
ValueCountFrequency (%)
) 7527
28.9%
( 7527
28.9%
4705
18.1%
. 3507
13.5%
$ 2351
 
9.0%
£ 437
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108917
99.6%
None 437
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 8855
 
8.1%
R 8826
 
8.1%
a 8732
 
8.0%
e 8037
 
7.4%
s 7786
 
7.1%
) 7527
 
6.9%
( 7527
 
6.9%
i 5800
 
5.3%
4705
 
4.3%
d 4661
 
4.3%
Other values (26) 36461
33.5%
None
ValueCountFrequency (%)
£ 437
100.0%

Has Table booking
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
0
7059 
1
 
468

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7527
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7059
93.8%
1 468
 
6.2%

Length

2024-01-03T19:41:43.486209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T19:41:43.855345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7059
93.8%
1 468
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 7059
93.8%
1 468
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7527
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7059
93.8%
1 468
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 7527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7059
93.8%
1 468
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7059
93.8%
1 468
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
0
4874 
1
2653 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7527
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4874
64.8%
1 2653
35.2%

Length

2024-01-03T19:41:44.362808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T19:41:44.712590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4874
64.8%
1 2653
35.2%

Most occurring characters

ValueCountFrequency (%)
0 4874
64.8%
1 2653
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7527
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4874
64.8%
1 2653
35.2%

Most occurring scripts

ValueCountFrequency (%)
Common 7527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4874
64.8%
1 2653
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4874
64.8%
1 2653
35.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
0
6215 
1
1312 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7527
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6215
82.6%
1 1312
 
17.4%

Length

2024-01-03T19:41:45.106576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T19:41:45.454371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6215
82.6%
1 1312
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 6215
82.6%
1 1312
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7527
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6215
82.6%
1 1312
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 7527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6215
82.6%
1 1312
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6215
82.6%
1 1312
 
17.4%

Switch to order menu
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
0
7527 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7527
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7527
100.0%

Length

2024-01-03T19:41:45.876899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T19:41:46.200634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7527
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7527
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7527
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7527
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7527
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7527
100.0%

Price range
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
2
2584 
3
2359 
4
1642 
1
942 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7527
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 2584
34.3%
3 2359
31.3%
4 1642
21.8%
1 942
 
12.5%

Length

2024-01-03T19:41:46.386783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T19:41:46.694527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2584
34.3%
3 2359
31.3%
4 1642
21.8%
1 942
 
12.5%

Most occurring characters

ValueCountFrequency (%)
2 2584
34.3%
3 2359
31.3%
4 1642
21.8%
1 942
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7527
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2584
34.3%
3 2359
31.3%
4 1642
21.8%
1 942
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 7527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2584
34.3%
3 2359
31.3%
4 1642
21.8%
1 942
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2584
34.3%
3 2359
31.3%
4 1642
21.8%
1 942
 
12.5%

Aggregate rating
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.118055
Minimum0
Maximum4.9
Zeros129
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:47.082514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2
Q14
median4.2
Q34.5
95-th percentile4.8
Maximum4.9
Range4.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.68019087
Coefficient of variation (CV)0.16517285
Kurtosis20.607593
Mean4.118055
Median Absolute Deviation (MAD)0.3
Skewness-3.8887868
Sum30996.6
Variance0.46265962
MonotonicityNot monotonic
2024-01-03T19:41:47.528984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.2 816
10.8%
4.3 805
10.7%
4.1 774
10.3%
4.4 703
9.3%
4.5 643
8.5%
4 635
8.4%
4.6 510
 
6.8%
3.9 480
 
6.4%
4.7 351
 
4.7%
3.8 316
 
4.2%
Other values (20) 1494
19.8%
ValueCountFrequency (%)
0 129
1.7%
2.1 1
 
< 0.1%
2.2 3
 
< 0.1%
2.3 7
 
0.1%
2.4 3
 
< 0.1%
2.5 11
 
0.1%
2.6 17
 
0.2%
2.7 8
 
0.1%
2.8 14
 
0.2%
2.9 15
 
0.2%
ValueCountFrequency (%)
4.9 257
 
3.4%
4.8 227
 
3.0%
4.7 351
4.7%
4.6 510
6.8%
4.5 643
8.5%
4.4 703
9.3%
4.3 805
10.7%
4.2 816
10.8%
4.1 774
10.3%
4 635
8.4%

Rating color
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
5BA829
3733 
3F7E00
1988 
9ACD32
1242 
CDD614
 
356
CBCBC8
 
129
Other values (2)
 
79

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters45162
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3F7E00
2nd row3F7E00
3rd row3F7E00
4th row5BA829
5th row5BA829

Common Values

ValueCountFrequency (%)
5BA829 3733
49.6%
3F7E00 1988
26.4%
9ACD32 1242
 
16.5%
CDD614 356
 
4.7%
CBCBC8 129
 
1.7%
FFBA00 65
 
0.9%
FF7800 14
 
0.2%

Length

2024-01-03T19:41:47.879249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T19:41:48.109352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
5ba829 3733
49.6%
3f7e00 1988
26.4%
9acd32 1242
 
16.5%
cdd614 356
 
4.7%
cbcbc8 129
 
1.7%
ffba00 65
 
0.9%
ff7800 14
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 5040
11.2%
2 4975
11.0%
9 4975
11.0%
0 4134
9.2%
B 4056
9.0%
8 3876
8.6%
5 3733
8.3%
3 3230
7.2%
F 2146
 
4.8%
7 2002
 
4.4%
Other values (6) 6995
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27993
62.0%
Uppercase Letter 17169
38.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4975
17.8%
9 4975
17.8%
0 4134
14.8%
8 3876
13.8%
5 3733
13.3%
3 3230
11.5%
7 2002
7.2%
6 356
 
1.3%
1 356
 
1.3%
4 356
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
A 5040
29.4%
B 4056
23.6%
F 2146
12.5%
E 1988
 
11.6%
C 1985
 
11.6%
D 1954
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 27993
62.0%
Latin 17169
38.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4975
17.8%
9 4975
17.8%
0 4134
14.8%
8 3876
13.8%
5 3733
13.3%
3 3230
11.5%
7 2002
7.2%
6 356
 
1.3%
1 356
 
1.3%
4 356
 
1.3%
Latin
ValueCountFrequency (%)
A 5040
29.4%
B 4056
23.6%
F 2146
12.5%
E 1988
 
11.6%
C 1985
 
11.6%
D 1954
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 5040
11.2%
2 4975
11.0%
9 4975
11.0%
0 4134
9.2%
B 4056
9.0%
8 3876
8.6%
5 3733
8.3%
3 3230
7.2%
F 2146
 
4.8%
7 2002
 
4.4%
Other values (6) 6995
15.5%

Rating text
Categorical

IMBALANCE 

Distinct28
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
Very Good
3678 
Excellent
1955 
Good
1228 
Average
413 
Not rated
 
129
Other values (23)
 
124

Length

Max length13
Median length9
Mean length8.0605819
Min length3

Characters and Unicode

Total characters60672
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowExcellent
2nd rowExcellent
3rd rowExcellent
4th rowVery Good
5th rowVery Good

Common Values

ValueCountFrequency (%)
Very Good 3678
48.9%
Excellent 1955
26.0%
Good 1228
 
16.3%
Average 413
 
5.5%
Not rated 129
 
1.7%
Excelente 22
 
0.3%
Velmi dobré 18
 
0.2%
Poor 14
 
0.2%
Çok iyi 11
 
0.1%
Biasa 8
 
0.1%
Other values (18) 51
 
0.7%

Length

2024-01-03T19:41:48.366379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
good 4906
43.1%
very 3678
32.3%
excellent 1955
 
17.2%
average 413
 
3.6%
not 129
 
1.1%
rated 129
 
1.1%
excelente 22
 
0.2%
dobré 22
 
0.2%
velmi 18
 
0.2%
poor 14
 
0.1%
Other values (22) 107
 
0.9%

Most occurring characters

ValueCountFrequency (%)
o 10040
16.5%
e 8651
14.3%
d 5067
8.4%
G 4906
8.1%
r 4271
7.0%
l 3962
 
6.5%
3866
 
6.4%
V 3702
 
6.1%
y 3699
 
6.1%
t 2249
 
3.7%
Other values (31) 10259
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45587
75.1%
Uppercase Letter 11219
 
18.5%
Space Separator 3866
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 10040
22.0%
e 8651
19.0%
d 5067
11.1%
r 4271
9.4%
l 3962
 
8.7%
y 3699
 
8.1%
t 2249
 
4.9%
n 1994
 
4.4%
c 1981
 
4.3%
x 1977
 
4.3%
Other values (16) 1696
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
G 4906
43.7%
V 3702
33.0%
E 1978
17.6%
A 413
 
3.7%
N 129
 
1.1%
B 35
 
0.3%
P 14
 
0.1%
M 12
 
0.1%
S 11
 
0.1%
Ç 11
 
0.1%
Other values (4) 8
 
0.1%
Space Separator
ValueCountFrequency (%)
3866
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56806
93.6%
Common 3866
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 10040
17.7%
e 8651
15.2%
d 5067
8.9%
G 4906
8.6%
r 4271
7.5%
l 3962
 
7.0%
V 3702
 
6.5%
y 3699
 
6.5%
t 2249
 
4.0%
n 1994
 
3.5%
Other values (30) 8265
14.5%
Common
ValueCountFrequency (%)
3866
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60619
99.9%
None 53
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 10040
16.6%
e 8651
14.3%
d 5067
8.4%
G 4906
8.1%
r 4271
7.0%
l 3962
 
6.5%
3866
 
6.4%
V 3702
 
6.1%
y 3699
 
6.1%
t 2249
 
3.7%
Other values (24) 10206
16.8%
None
ValueCountFrequency (%)
é 24
45.3%
Ç 11
20.8%
ě 6
 
11.3%
á 4
 
7.5%
ľ 4
 
7.5%
İ 2
 
3.8%
ú 2
 
3.8%

Votes
Real number (ℝ)

Distinct1739
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean616.40149
Minimum0
Maximum41333
Zeros15
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size58.9 KiB
2024-01-03T19:41:48.629794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q1152
median335
Q3663
95-th percentile2064.9
Maximum41333
Range41333
Interquartile range (IQR)511

Descriptive statistics

Standard deviation1127.6979
Coefficient of variation (CV)1.8294861
Kurtosis260.75978
Mean616.40149
Median Absolute Deviation (MAD)226
Skewness10.668222
Sum4639654
Variance1271702.6
MonotonicityNot monotonic
2024-01-03T19:41:49.005251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 52
 
0.7%
7 44
 
0.6%
5 43
 
0.6%
11 42
 
0.6%
2 40
 
0.5%
6 39
 
0.5%
1 36
 
0.5%
14 31
 
0.4%
4 30
 
0.4%
12 28
 
0.4%
Other values (1729) 7142
94.9%
ValueCountFrequency (%)
0 15
 
0.2%
1 36
0.5%
2 40
0.5%
3 52
0.7%
4 30
0.4%
5 43
0.6%
6 39
0.5%
7 44
0.6%
8 23
0.3%
9 20
 
0.3%
ValueCountFrequency (%)
41333 1
< 0.1%
17394 1
< 0.1%
15270 1
< 0.1%
14984 1
< 0.1%
13627 1
< 0.1%
12443 2
< 0.1%
11910 1
< 0.1%
11836 1
< 0.1%
11476 1
< 0.1%
10892 1
< 0.1%

Interactions

2024-01-03T19:41:26.750901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:15.926139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:17.667664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:19.485724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:21.310681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:23.043534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:24.944025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:26.973099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:16.197822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:17.876506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:19.707378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:21.622550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:23.276603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:25.168820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:27.164814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:16.399626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:18.162528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:19.949168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:21.897859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:23.578062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:25.372535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:27.361848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:16.629462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:18.437403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:20.234941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:22.218278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:23.819785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:25.604497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:27.575878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:16.930316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:18.722895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:20.444154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:22.425252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:24.046379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:25.879651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:27.785030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:17.234220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:19.020192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:20.736557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:22.644357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:24.351446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:26.177300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:28.001775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:17.450538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:19.300117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:21.028785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:22.846013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:24.652897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T19:41:26.467946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-03T19:41:28.427234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-03T19:41:29.342241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotes
06310675Mama Lou's Italian Kitchen162Las Piñas CityBlock 1, Lot 36, Tropical Avenue Corner Tropical Palace, BF International, Las Piñas CityBF InternationalBF International, Las Piñas City121.00978714.447615Italian1100Botswana Pula(P)100034.63F7E00Excellent619
16310675Mama Lou's Italian Kitchen162Las Piñas CityBlock 1, Lot 36, Tropical Avenue Corner Tropical Palace, BF International, Las Piñas CityBF InternationalBF International, Las Piñas City121.00978714.447615Italian1100Botswana Pula(P)100034.63F7E00Excellent619
26314542Blackbird162Makati CityNielson Tower, Ayala Triangle Gardens, Salcedo Village, Makati CityAyala Triangle Gardens, Salcedo Village, Makati CityAyala Triangle Gardens, Salcedo Village, Makati City, Makati City121.02456214.556042European, Asian3100Botswana Pula(P)000044.73F7E00Excellent469
36301293Banapple162Makati CityAyala Triangle Gardens, Salcedo Village, Makati CityAyala Triangle Gardens, Salcedo Village, Makati CityAyala Triangle Gardens, Salcedo Village, Makati City, Makati City121.02317114.556196Filipino, American, Italian, Bakery800Botswana Pula(P)000034.45BA829Very Good867
46315689Bad Bird162Makati CityHole In The Wall, Floor 4, Century City Mall, Kalayaan Avenue, Poblacion, Makati CityCentury City Mall, Poblacion, Makati CityCentury City Mall, Poblacion, Makati City, Makati City121.02770814.565899American700Botswana Pula(P)000034.45BA829Very Good858
56304833Manam162Makati CityLevel 1, Greenbelt 2, Ayala Center, Greenbelt, Makati CityGreenbelt 2, San Lorenzo, Makati CityGreenbelt 2, San Lorenzo, Makati City, Makati City121.02038014.552351Filipino700Botswana Pula(P)000034.73F7E00Excellent930
618409457Soban K-Town Grill162Makati CityLevel 3, Greenbelt 3, Ayala Center, Greenbelt, Makati CityGreenbelt 3, San Lorenzo, Makati CityGreenbelt 3, San Lorenzo, Makati City, Makati City121.02138814.552248Korean, Grill1300Botswana Pula(P)000034.73F7E00Excellent935
718607559Bluesmith Coffee & Kitchen162Makati CityLevel 3, Greenbelt 3, Ayala Center, Greenbelt, Makati CityGreenbelt 3, San Lorenzo, Makati CityGreenbelt 3, San Lorenzo, Makati City, Makati City121.02137414.552044American, Filipino, Coffee700Botswana Pula(P)000034.05BA829Very Good340
86314001Motorino Pizzeria Napoletana162Makati CityLevel 2, Greenbelt 3, Ayala Center, Greenbelt, Makati CityGreenbelt 3, San Lorenzo, Makati CityGreenbelt 3, San Lorenzo, Makati City, Makati City121.02177214.551875Pizza, Italian1000Botswana Pula(P)011034.35BA829Very Good449
918189398Shi Lin162Makati CityLevel 3, Greenbelt 3, Ayala Center, Greenbelt, Makati CityGreenbelt 3, San Lorenzo, Makati CityGreenbelt 3, San Lorenzo, Makati City, Makati City121.02165314.552189Taiwanese1000Botswana Pula(P)010034.15BA829Very Good201
Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotes
751718384475Happy Moon's208İstanbulWatergarden Yaşam Merkezi, Barbaros Mahallesi, Şebboy Sokak, No 2, Ataşehir, İstanbulWatergarden, Batı Ataşehir, AtaşehirWatergarden, Batı Ataşehir, Ataşehir, İstanbul29.09975540.998032World Cuisine200Turkish Lira(TL)000044.45BA829Very Good503
751818384475Happy Moon's208İstanbulWatergarden Yaşam Merkezi, Barbaros Mahallesi, Şebboy Sokak, No 2, Ataşehir, İstanbulWatergarden, Batı Ataşehir, AtaşehirWatergarden, Batı Ataşehir, Ataşehir, İstanbul29.09975540.998032World Cuisine200Turkish Lira(TL)000044.45BA829Very Good503
751918384621The Hunger Cafe & Brasserie208İstanbulWatergarden Yaşam Merkezi, Barbaros Mahallesi, Şebboy Sokak, No 2, Ataşehir, İstanbulWatergarden, Batı Ataşehir, AtaşehirWatergarden, Batı Ataşehir, Ataşehir, İstanbul29.09975140.998086World Cuisine190Turkish Lira(TL)000044.45BA829Very Good557
752018264711Ercan Steakhouse208İstanbulZiya Gökalp Mahallesi, Atatürk Bulvarı, No 112, Başakşehir, İstanbulZiya GökalpZiya Gökalp, İstanbul28.80282041.088293Steak200Turkish Lira(TL)000044.15BA829Very Good503
752118286221Sarıhan208İstanbulİkitelli OSB Mahallesi, Atatürk Bulvarı, No 98, Başakşehir, İstanbulZiya GökalpZiya Gökalp, İstanbul28.80486841.084974Giblets, Izgara, Home-made100Turkish Lira(TL)000034.45BA829Very Good500
75225912546Eataly208İstanbulZorlu Center AVM, Köprü Katı, Levazım Mahallesi, Barbaros Bulvarı Kavşağı, Koru Sokak, No 2, Beşiktaş, İstanbulZorlu Center AVM, Levazım, BeşiktaşZorlu Center AVM, Levazım, Beşiktaş, İstanbul29.01732641.065322Italian, Pizza, Fresh Fish300Turkish Lira(TL)000044.35BA829Very Good1367
75235913006Tarihi Çınaraltı Aile Çay Bahçesi208İstanbulÇengelköy Mahallesi, Çınaraltı Camii Sokak, No 4, Üsküdar, İstanbulÇengelköy MerkezÇengelköy Merkez, İstanbul29.05262041.050280Fast Food, Izgara, Seafood, Tea, Coffee45Turkish Lira(TL)000024.53F7E00Excellent1172
75245923535Boon Cafe & Restaurant208İstanbulÇengelköy Mahallesi, Çengelköy Caddesi, Kara Sokak, No 1/A, Üsküdar, İstanbulÇengelköy MerkezÇengelköy Merkez, İstanbul29.05262341.050717Restaurant Cafe140Turkish Lira(TL)000044.25BA829Very Good1160
75255914190Kanaat Lokantası208İstanbulSultantepe Mahallesi, Selmani Pak Caddesi, No 9, Üsküdar, İstanbulÜsküdar MerkezÜsküdar Merkez, İstanbul29.01659041.025741Home-made, Izgara95Turkish Lira(TL)000034.05BA829Very Good770
75265913508Katibim208İstanbulMimar Sinan Mahallesi, Harem Sahilyolu, No 53, Üsküdar, İstanbulÜsküdar MerkezÜsküdar Merkez, İstanbul29.01080841.025501Restaurant Cafe, Kebab, Turkish Pizza150Turkish Lira(TL)000044.25BA829Very Good1141

Duplicate rows

Most frequently occurring

Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotes# duplicates
2573900267Roma's Café Diner1Varanasi2nd Floor, Swastik Plaza, Lanka, VaranasiLankaLanka, Varanasi83.00328525.281255Continental, Cafe, North Indian, Italian, Asian, Mediterranean, Mexican800Indian Rupees(Rs.)011034.63F7E00Excellent8773
53118691230Grandmama's Café1MumbaiGround Floor, Courtyard R-City Mall, Ghatkopar West, MumbaiR City Mall, Ghatkopar WestR City Mall, Ghatkopar West, Mumbai72.91639419.099912Cafe, American, Italian, Parsi, Desserts1200Indian Rupees(Rs.)110034.35BA829Very Good11083
04751Chili's Grill & Bar1New Delhi3rd Floor, Ambience Mall, Nelson Madela Road, Vasant Kunj, New DelhiAmbience Mall, Vasant KunjAmbience Mall, Vasant Kunj, New Delhi77.15500728.540897Italian, Finger Food1500Indian Rupees(Rs.)010034.83F7E00Excellent58982
18913Pirates of Grill1GurgaonGround Floor, MGF Mega City Mall, MG Road, GurgaonMGF Mega City Mall, MG RoadMGF Mega City Mall, MG Road, Gurgaon77.08943128.479873North Indian, Mughlai, Continental2000Indian Rupees(Rs.)100044.93F7E00Skvělé57602
213019Aroma's Hyderabad House1PuneOpposite Fire Station, Near Megapolis Bus Stop, Phase 3, IT Park, Hinjawadi, PuneHinjawadiHinjawadi, Pune73.68826618.575457Biryani, North Indian, Mughlai750Indian Rupees(Rs.)010024.05BA829Very Good7602
315078Domino's Pizza1LudhianaSCF 21, Main Market, Sarabha Nagar, LudhianaMain Market, Sarabha NagarMain Market, Sarabha Nagar, Ludhiana75.82217930.892660Pizza, Fast Food400Indian Rupees(Rs.)000014.05BA829Very Good9532
415084Sonu's Kitchen1LudhianaSunet, Near Water Tank, BRS Nagar, LudhianaBRS NagarBRS Nagar, Ludhiana75.79802330.885037North Indian, Chinese, Fast Food, Italian1000Indian Rupees(Rs.)011034.15BA829Very Good7352
515290Domino's Pizza1LudhianaSCF 24, Urban Estate, Main Market, Phase 1, Dugri, LudhianaA Hotel, Gurdev NagarA Hotel, Gurdev Nagar, Ludhiana75.84243430.873862Pizza, Fast Food400Indian Rupees(Rs.)000014.05BA829Very Good15232
615363Domino's Pizza1LudhianaSCO 25, Chandigarh Road, Sector 32 A, Sector 32, LudhianaSector 32Sector 32, Ludhiana75.89461030.905730Pizza, Fast Food400Indian Rupees(Rs.)000013.79ACD32Good18102
715436Captain Sam's1Ludhiana28 F, Malhar Road, Gurdev Nagar, LudhianaGurdev NagarGurdev Nagar, Ludhiana75.82212630.895279Pizza, Fast Food700Indian Rupees(Rs.)011023.89ACD32Good3682